Resource scheduling optimization in cloud-edge collaboration

Shuling WANG; Jie SUN; Peng WANG; Aidong YANG

doi:10.11959/j.issn.1000-0801.2023027

您当前的位置：

首页 >

文章列表页 >

Resource scheduling optimization in cloud-edge collaboration

Engineering and Application | 更新时间：2024-06-05

- Resource scheduling optimization in cloud-edge collaboration
- Telecommunications Science Vol. 39, Issue 2, Pages: 163-170(2023)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2023027
  CLC： TP393
- Published Online：2023-02，
  
  Published：20 February 2023
- 稿件说明：
移动端阅览
Shuling WANG, Jie SUN, Peng WANG, et al. Resource scheduling optimization in cloud-edge collaboration[J]. Telecommunications science, 2023, 39(2): 163-170.
DOI：

Shuling WANG, Jie SUN, Peng WANG, et al. Resource scheduling optimization in cloud-edge collaboration[J]. Telecommunications science, 2023, 39(2): 163-170. DOI： 10.11959/j.issn.1000-0801.2023027.

摘要

随着业务类型的丰富和多样化，低时延、高带宽、数据私密性、高可靠性等成为业务普遍的要求。边缘计算、雾计算、分布式云、算力网络等方案相继被提出，并在产学研各界引发了深度的研究和探索。针对“多级的算力分布以及算力的协同将是未来算力结构的主流”这一观点，产业内外达成了共识，算力管理、分配、调度等与资源优化相关的问题也成为当下的研究热点和重点攻关方向。为此，面向未来的算力供给结构，首先描述了学术界、产业界资源调度优化问题的最新进展，总结了当前的主要方法论和工程实施架构；然后，针对两种典型的云边协同场景，从场景拆分、调度目标、求解方案依次进行分析，给出了适应场景特性的资源调度优化参考方案。

Abstract

With the enrichment and diversification of business types

low latency

high bandwidth

data privacy and high reliability have become common requirements.Edge computing

fog computing

distributed cloud

computing power network and other solutions have been proposed

and have triggered in-depth research and exploration in industry

academia and research.There is a consensus within and outside the industry on the view that “multi-level computing power distribution and collaboration of computing power will be the mainstream of computing power structure in the future”.The problems related to resource scheduling optimization

such as computing power management

allocation

scheduling

have also become the current research hotspot and key research direction.Therefore

for the future computing power supply structure

focuses on the latest progress of resource scheduling optimization in academia and industry

the current main methodology and engineering implementation architecture was summarized.And then

for the two typical cloud edge collaboration scenarios

the analysis was carried out from the perspective of scene splitting

scheduling objectives

and solutions in turn

and the resource scheduling optimization reference schemes that adapted to the characteristics of the scenarios were analyzed and discussed respectively.

关键词

Keywords

references

ETSI . Multi-access edge computing (MEC) [EB ] . 2022 .

HU P F , DHELIM S , NING H S , et al . Survey on fog computing:architecture,key technologies,applications and open issues [J ] . Journal of Network and Computer Applications , 2017 ( 98 ): 27 - 42 .

MARTIN A . Distributed computing:utilities,grids ＆ clouds ITU-T technology watch report 2009 [R ] . 2009 .

ITU-T.Y . 2501:computing power network-framework and architecture [S ] . 2019 .

王凌 , 吴楚格 , 范文慧 . 边缘计算资源分配与任务调度优化综述 [J ] . 系统仿真学报 , 2021 , 33 ( 3 ): 509 - 520 .

WANG L , WU C G , FAN W H . A survey of edge computing resource allocation and task scheduling optimization [J ] . Journal of System Simulation , 2021 , 33 ( 3 ): 509 - 520 .

JAMIL B , IJAZ H , SHOJAFAR M , et al . Resource allocation and task scheduling in fog computing and Internet of everything environments:a taxonomy,review,and future directions [J ] . ACM Computing Surveys , 2022 , 54 ( 11s ): 1 - 38 .

IBRAHIM E , EL-BAHNASAWY N A , OMARA F A . Task scheduling algorithm in cloud computing environment based on cloud pricing models [C ] // Proceedings of 2016 World Symposium on Computer Applications ＆ Research (WSCAR) . Piscataway:IEEE Press , 2016 : 65 - 71 .

ABDULLAHI C , GOUR K , JOARDER K . The co-evolution of cloud and IoT applications:recent and future trends [R ] . 2019 .

BENBLIDIA M A , BRIK B , MERGHEM-BOULAHIA L , et al . Ranking fog nodes for tasks scheduling in fog-cloud environments:a fuzzy logic approach [C ] // Proceedings of 2019 15th International Wireless Communications ＆ Mobile Computing Conference (IWCMC) . Piscataway:IEEE Press , 2019 : 1451 - 1457 .

ABDELMONEEM R M , BENSLIMANE A , SHAABAN E . Mobility-aware task scheduling in cloud-Fog IoT-based healthcare architectures [J ] . Computer Networks , 2020 ( 179 ): 107348 .

NI L N , ZHANG J Q , JIANG C J , et al . Resource allocation strategy in fog computing based on priced timed petri nets [J ] . IEEE Internet of Things Journal , 2017 , 4 ( 5 ): 1216 - 1228 .

ZHAO X Y , ZONG Q , TIAN B L , et al . Fast task allocation for heterogeneous unmanned aerial vehicles through reinforcement learning [J ] . Aerospace Science and Technology , 2019 ( 92 ): 588 - 594 .

Gartner . Gartner trends 2021:what they mean for retailers [R ] . 2020 .

Cloud Networking . The 2020 gartner magic quadrant for data center and cloud networking [R ] . 2019 .

中国电信 . 云网融合2030技术白皮书 [R ] . 2020 .

China Telecom . Computing and network convergence technical white paper [R ] . 2020 .

RAUSCH T , RASHED A , DUSTDAR S . Optimized container scheduling for data-intensive server less edge computing [J ] . Future Generation Computer Systems , 2021 ( 114 ): 259 - 271 .

XU J L , PALANISAMY B , LUDWIG H , et al . Zenith:utility-aware resource allocation for edge computing [C ] // Proceedings of 2017 IEEE International Conference on Edge Computing (EDGE) . Piscataway:IEEE Press , 2017 : 47 - 54 .

CHEN J S , BALASUBRAMANIAN B , HUANG Z . Liv(e)-ing on the edge:user-uploaded live streams driven by “first-Mile”edge decisions [C ] // Proceedings of 2019 IEEE International Conference on Edge Computing (EDGE) . Piscataway:IEEE Press , 2019 : 41 - 50 .

FARHADI V , MEHMETI F , HE T , et al . Service placement and request scheduling for data-intensive applications in edge clouds [J ] . IEEE/ACM Transactions on Networking , 2021 , 29 ( 2 ): 779 - 792 .

ADDYA S K , SATPATHY A , GHOSH B C , et al . CoMCLOUD:virtual machine coalition for multi-tier applications over multi-cloud environments [J ] . IEEE Transactions on Cloud Computing , 2021 ( 99 ): 1 .

Views

1164

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Multi-user fine-grained task offloading scheduling strategy under cloud-edge-end collaboration

Edge computing smart grid resource scheduling algorithm based on reinforcement learning

A survey on 5G capabilities enabling the factories of the future

Development status and trend prospect of computing power infrastructure

Application of 5G in railway smart stations

Related Author

XIE Mande

HUANG Zhufang

SUN Hao

Jinghang YU

Yichen ZHAO

Hu SONG

Yusun FU

Jinhui TANG

Related Institution

School of Information and Electronic Engineering, Zhejiang Gongshang University

School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University

Key Laboratory of System Control and Information Processing, Ministry of Education of China

Shanghai Engineering Research Center of Intelligent Control and Management

Beijing Jingwei Information Technology Co.,Ltd.

⁰